Add LLM filtering pattern, .env.example, and workflows/lib

- Add .env.example with LLM_API_URL, LLM_MODEL, LLM_API_KEY
- Add .gitignore to exclude .env
- Add Pattern 5 (LLM filtering) to notebook-patterns.md
- Track workflows/lib with llm_call helper using mirascope
- Update README with LLM setup step and updated project structure

workflows/lib/__init__.py

@@ -0,0 +1,5 @@
+"""Library modules for contact analysis workbooks."""
+
+from lib.llm import llm_call
+
+__all__ = ["llm_call"]

workflows/lib/llm.py

@@ -0,0 +1,51 @@
+"""Simple LLM helper for workbooks using Mirascope."""
+
+import os
+from typing import TypeVar
+
+from mirascope.core import Messages, openai
+from pydantic import BaseModel
+
+T = TypeVar("T", bound=BaseModel)
+
+# Configure from environment (defaults match .env.example)
+_api_key = os.getenv("LLM_API_KEY", "")
+_base_url = os.getenv("LLM_API_URL", "https://litellm-notrack.app.monadical.io")
+_model = os.getenv("LLM_MODEL", "GLM-4.5-Air-FP8-dev")
+
+if _api_key:
+    os.environ["OPENAI_API_KEY"] = _api_key
+if _base_url:
+    base = _base_url.rstrip("/")
+    os.environ["OPENAI_BASE_URL"] = base if base.endswith("/v1") else f"{base}/v1"
+
+
+async def llm_call(
+    prompt: str,
+    response_model: type[T],
+    system_prompt: str = "You are a helpful assistant.",
+    model: str | None = None,
+) -> T:
+    """Make a structured LLM call.
+
+    Args:
+        prompt: The user prompt
+        response_model: Pydantic model for structured output
+        system_prompt: System instructions
+        model: Override the default model
+
+    Returns:
+        Parsed response matching the response_model schema
+    """
+    use_model = model or _model
+
+    @openai.call(model=use_model, response_model=response_model)
+    async def _call(sys: str, usr: str) -> openai.OpenAIDynamicConfig:
+        return {
+            "messages": [
+                Messages.System(sys),
+                Messages.User(usr),
+            ]
+        }
+
+    return await _call(system_prompt, prompt)